scholarly journals IPOMOEA: Intended package orientation using multi-objective evolutionary algorithm in R

2019 ◽  
Author(s):  
M A El-dosuky
Keyword(s):  
Evolutionary Algorithm ◽  
Programming Language ◽  
Specific Task ◽  
Nsga Ii ◽  
R Language ◽  
Multi Objective

Programmers’ lack of familiarity with what is available in packages may prompt them to reinvent the wheel. This is generally the case in any programming language, but it is a matter of madness with a language described as difficult even by professionals supporting it such as R. In R Cookbook, says: “But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones.” IPOMOEA is a code that has been written to mitigate this problem. It helps R language developers determine how to perform a specific task, by automating the search in R site for all packages that are likely to contribute to the task implementation. After that, IPOMOEA determines a partial set of results to be the intended package using multi-objective evolutionary algorithm NSGA-II . Not only does it specify the intended package, but also it helps orient programmers and manage packages. Keywords:

scholarly journals IPOMOEA: Intended package orientation using multi-objective evolutionary algorithm in R

2019 ◽  
Author(s):  
M A El-dosuky
Keyword(s):  
Evolutionary Algorithm ◽  
Programming Language ◽  
Specific Task ◽  
Nsga Ii ◽  
R Language ◽  
Multi Objective

Programmers’ lack of familiarity with what is available in packages may prompt them to reinvent the wheel. This is generally the case in any programming language, but it is a matter of madness with a language described as difficult even by professionals supporting it such as R. In R Cookbook, says: “But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones.” IPOMOEA is a code that has been written to mitigate this problem. It helps R language developers determine how to perform a specific task, by automating the search in R site for all packages that are likely to contribute to the task implementation. After that, IPOMOEA determines a partial set of results to be the intended package using multi-objective evolutionary algorithm NSGA-II . Not only does it specify the intended package, but also it helps orient programmers and manage packages. Keywords:

scholarly journals Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

2021 ◽  
pp. 1-34
Author(s):  
Joost Huizinga ◽  
Jeff Clune
Keyword(s):  
Evolutionary Algorithm ◽  
Function Optimization ◽  
Stepping Stones ◽  
Nsga Ii ◽  
Navigation Problem ◽  
Multi Objective ◽  
Robot Locomotion ◽  
Important Challenge ◽  
Maze Navigation ◽  
Multimodal Problems

Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of sub-tasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III and ϵ-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks and a simulated robot maze navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions

2005 ◽  
Vol 13 (4) ◽  
pp. 501-525 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Manikanth Mohan ◽  
Shikhar Mishra
Keyword(s):  
Steady State ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computational Time ◽  
Test Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Nsga Ii ◽  
Multi Objective

Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the ε-dominance concept introduced earlier (Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the ε-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search

2016 ◽  
Vol 25 (4) ◽  
pp. 859-878 ◽  
Author(s):  
Ernestas Filatovas ◽  
Algirdas Lančinskas ◽  
Olga Kurasova ◽  
Julius Žilinskas
Keyword(s):  
Local Search ◽  
Evolutionary Algorithm ◽  
Stochastic Local Search ◽  
Nsga Ii ◽  
Multi Objective

scholarly journals A novel application of a multi-objective evolutionary algorithm in open channel flow modelling

2009 ◽  
Vol 11 (1) ◽  
pp. 31-50 ◽  
Author(s):  
S. Sharifi ◽  
M. Sterling ◽  
D. W. Knight
Keyword(s):  
Evolutionary Algorithm ◽  
Computational Effort ◽  
Detailed Knowledge ◽  
Lateral Variation ◽  
Nsga Ii ◽  
Multi Objective ◽  
Boundary Shear ◽  
Aspect Ratios ◽  
Sink Term ◽  
Wide Range

The Shiono and Knight method (SKM) is a simple depth-averaged flow model, based on the RANS equations which can be used to estimate the lateral distributions of depth-averaged velocity and boundary shear stress for flows in straight prismatic channels with the minimum of computational effort. However, in order to apply the SKM, detailed knowledge relating to the lateral variation of the friction factor (f), dimensionless eddy viscosity (λ) and a sink term representing the effects of secondary flow (Γ) are required. In this paper a multi-objective evolutionary algorithm is used to study the lateral variation and value of these parameters for simple trapezoidal channels over a wide range of aspect ratios through the model calibration process. Based on the available experimental data, four objectives are selected and the NSGA-II algorithm is applied to several datasets. The best answer for each set is then selected based on a proposed methodology. Rules relating f, λ and Γ to the wetted parameter ratio (Pb/Pw) for a variety of situations have been developed which provide practical guidance for the engineer on choosing the appropriate parameters in the SKM model.

scholarly journals Centralized Unmanned Aerial Vehicle (UAV) Mesh Networks Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach

2018 ◽  
Author(s):  
Sérgio Sabino ◽  
António Grilo
Keyword(s):  
Evolutionary Algorithm ◽  
Mesh Networks ◽  
Signal To Noise Ratio ◽  
Geographical Area ◽  
Cost Effective ◽  
Data Rate ◽  
Mesh Network ◽  
Target Area ◽  
Nsga Ii ◽  
Multi Objective

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in the military operations to prevent pilot losses. Nowadays, the fast technological evolution enables the production of a class of cost-effective UAVs which can service a plethora of public and civilian applications, specially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

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